Research Interests

Our
research group
focuses on studying and understanding 1) the
underlying principles of biological computation, and how these
principles can be adopted or modified to extend contemporary
computer science methods, and 2) automated causal reasoning,
such as abductive inference and Bayesian/belief networks.

Several properties of
biologically-inspired computing
separate it from more traditional
computer science, giving hope that new robust and adaptive
software methods can be developed. Examples of this type of
computing include neural computation, evolutionary computation,
artificial life, self-replicating machines, artificial immune
systems, ant colony optimization, L-systems, artificial
societies, and swarm intelligence. Our group has worked and/or is
working in the following areas:

neural computation

multi-agent artificial life systems

evolutionary computation

cellular automata models of self-replication

We are also focusing on automated
causal reasoning
using more traditional methods in artificial intelligence.
The goal of this research is to
model human cognition as a means of generating useful automated
reasoning systems. Our group has worked and/or is working
in the following areas: